Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving

תקציר מקורי באנגליתarXiv:2508.16947v2 Announce Type: replace-cross Abstract: Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency biased behaviors, overlooking the inherent behavioral diversity of human driving. Moreover, existing systems struggle to understand user intent from human interactions and environmental contexts. In real-world advanced deployment, motion planning must accommodate diverse, context-dependent user preferences to support heterogeneous driving services, requiring the ability to interpret human intent and adapt behavior accordingly. However, existing approaches lack such user-oriented capabilities, as they neither explicitly model user intent nor enable flexible policy adaptation. To bridge this gap, w